Overview

Dataset statistics

Number of variables16
Number of observations8789
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory286.5 B

Variable types

Text2
Numeric14

Alerts

PC1 is highly overall correlated with protein_gHigh correlation
PC10 is highly overall correlated with PC3 and 1 other fieldsHigh correlation
PC2 is highly overall correlated with calories and 1 other fieldsHigh correlation
PC3 is highly overall correlated with PC10 and 2 other fieldsHigh correlation
PC4 is highly overall correlated with protein_gHigh correlation
calories is highly overall correlated with PC2 and 1 other fieldsHigh correlation
carbohydrate_g is highly overall correlated with PC10 and 1 other fieldsHigh correlation
protein_g is highly overall correlated with PC1 and 2 other fieldsHigh correlation
total_fat_grams is highly overall correlated with PC2 and 1 other fieldsHigh correlation
name has unique valuesUnique
total_fat_grams has 451 (5.1%) zerosZeros
protein_g has 358 (4.1%) zerosZeros
carbohydrate_g has 2158 (24.6%) zerosZeros

Reproduction

Analysis started2024-06-12 01:45:53.079806
Analysis finished2024-06-12 01:46:09.407231
Duration16.33 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

name
Text

UNIQUE 

Distinct8789
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size956.1 KiB
2024-06-11T20:46:09.619127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length134
Median length101
Mean length54.378314
Min length3

Characters and Unicode

Total characters477931
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8789 ?
Unique (%)100.0%

Sample

1st rowCornstarch
2nd rowNuts, pecans
3rd rowEggplant, raw
4th rowTeff, uncooked
5th rowSherbet, orange
ValueCountFrequency (%)
and 2097
 
3.0%
fat 2054
 
3.0%
cooked 1785
 
2.6%
lean 1513
 
2.2%
separable 1457
 
2.1%
raw 1390
 
2.0%
with 1385
 
2.0%
beef 1161
 
1.7%
trimmed 995
 
1.4%
to 973
 
1.4%
Other values (3293) 54011
78.5%
2024-06-11T20:46:09.998541image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
60053
 
12.6%
e 45938
 
9.6%
a 34165
 
7.1%
, 30332
 
6.3%
r 25907
 
5.4%
o 24616
 
5.2%
t 22189
 
4.6%
d 20052
 
4.2%
s 20023
 
4.2%
n 19904
 
4.2%
Other values (66) 174752
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 477931
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
60053
 
12.6%
e 45938
 
9.6%
a 34165
 
7.1%
, 30332
 
6.3%
r 25907
 
5.4%
o 24616
 
5.2%
t 22189
 
4.6%
d 20052
 
4.2%
s 20023
 
4.2%
n 19904
 
4.2%
Other values (66) 174752
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 477931
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
60053
 
12.6%
e 45938
 
9.6%
a 34165
 
7.1%
, 30332
 
6.3%
r 25907
 
5.4%
o 24616
 
5.2%
t 22189
 
4.6%
d 20052
 
4.2%
s 20023
 
4.2%
n 19904
 
4.2%
Other values (66) 174752
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 477931
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
60053
 
12.6%
e 45938
 
9.6%
a 34165
 
7.1%
, 30332
 
6.3%
r 25907
 
5.4%
o 24616
 
5.2%
t 22189
 
4.6%
d 20052
 
4.2%
s 20023
 
4.2%
n 19904
 
4.2%
Other values (66) 174752
36.6%

calories
Real number (ℝ)

HIGH CORRELATION 

Distinct671
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226.28388
Minimum0
Maximum902
Zeros39
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size68.8 KiB
2024-06-11T20:46:10.125074image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q191
median191
Q3337
95-th percentile519
Maximum902
Range902
Interquartile range (IQR)246

Descriptive statistics

Standard deviation169.862
Coefficient of variation (CV)0.75065888
Kurtosis1.7132432
Mean226.28388
Median Absolute Deviation (MAD)115
Skewness1.1490912
Sum1988809
Variance28853.1
MonotonicityNot monotonic
2024-06-11T20:46:10.233823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
884 78
 
0.9%
47 45
 
0.5%
56 43
 
0.5%
0 39
 
0.4%
50 38
 
0.4%
63 38
 
0.4%
53 37
 
0.4%
127 37
 
0.4%
20 36
 
0.4%
34 36
 
0.4%
Other values (661) 8362
95.1%
ValueCountFrequency (%)
0 39
0.4%
1 17
0.2%
2 11
 
0.1%
3 4
 
< 0.1%
4 12
 
0.1%
5 7
 
0.1%
6 6
 
0.1%
7 2
 
< 0.1%
8 4
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
902 9
 
0.1%
900 12
 
0.1%
899 1
 
< 0.1%
898 1
 
< 0.1%
897 1
 
< 0.1%
894 1
 
< 0.1%
892 1
 
< 0.1%
889 1
 
< 0.1%
884 78
0.9%
882 1
 
< 0.1%

total_fat_grams
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct176
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.556855
Minimum0
Maximum100
Zeros451
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size68.8 KiB
2024-06-11T20:46:10.346377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5.1
Q314
95-th percentile35
Maximum100
Range100
Interquartile range (IQR)13

Descriptive statistics

Standard deviation15.818247
Coefficient of variation (CV)1.4983863
Kurtosis13.754812
Mean10.556855
Median Absolute Deviation (MAD)4.8
Skewness3.3095707
Sum92784.2
Variance250.21695
MonotonicityNot monotonic
2024-06-11T20:46:10.467092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 451
 
5.1%
0.1 437
 
5.0%
0.2 354
 
4.0%
11 225
 
2.6%
0.3 215
 
2.4%
12 207
 
2.4%
0.4 201
 
2.3%
13 201
 
2.3%
14 156
 
1.8%
0.5 150
 
1.7%
Other values (166) 6192
70.5%
ValueCountFrequency (%)
0 451
5.1%
0.1 437
5.0%
0.2 354
4.0%
0.3 215
2.4%
0.4 201
2.3%
0.5 150
 
1.7%
0.6 104
 
1.2%
0.7 101
 
1.1%
0.8 90
 
1.0%
0.9 89
 
1.0%
ValueCountFrequency (%)
100 107
1.2%
99 5
 
0.1%
97 1
 
< 0.1%
94 3
 
< 0.1%
89 1
 
< 0.1%
84 1
 
< 0.1%
81 10
 
0.1%
80 8
 
0.1%
79 3
 
< 0.1%
78 3
 
< 0.1%

protein_g
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2664
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.345616
Minimum0
Maximum88.32
Zeros358
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size68.8 KiB
2024-06-11T20:46:10.582053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.07
Q12.38
median8.02
Q319.88
95-th percentile29.092
Maximum88.32
Range88.32
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation10.530602
Coefficient of variation (CV)0.92816481
Kurtosis2.6461171
Mean11.345616
Median Absolute Deviation (MAD)6.88
Skewness1.1657796
Sum99716.62
Variance110.89357
MonotonicityNot monotonic
2024-06-11T20:46:10.696097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 358
 
4.1%
0.1 45
 
0.5%
0.07 41
 
0.5%
0.3 40
 
0.5%
0.2 40
 
0.5%
5 31
 
0.4%
2 29
 
0.3%
0.4 28
 
0.3%
5.5 27
 
0.3%
6.5 27
 
0.3%
Other values (2654) 8123
92.4%
ValueCountFrequency (%)
0 358
4.1%
0.01 22
 
0.3%
0.02 3
 
< 0.1%
0.03 5
 
0.1%
0.04 3
 
< 0.1%
0.05 5
 
0.1%
0.06 6
 
0.1%
0.07 41
 
0.5%
0.08 2
 
< 0.1%
0.09 5
 
0.1%
ValueCountFrequency (%)
88.32 2
< 0.1%
85.6 1
< 0.1%
84.08 1
< 0.1%
82.6 1
< 0.1%
82.4 1
< 0.1%
81.1 1
< 0.1%
78.13 1
< 0.1%
77.27 1
< 0.1%
76.92 1
< 0.1%
75.16 1
< 0.1%

carbohydrate_g
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3322
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.121915
Minimum0
Maximum100
Zeros2158
Zeros (%)24.6%
Negative0
Negative (%)0.0%
Memory size68.8 KiB
2024-06-11T20:46:10.811580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.05
median9.34
Q334.91
95-th percentile78.686
Maximum100
Range100
Interquartile range (IQR)34.86

Descriptive statistics

Standard deviation27.266261
Coefficient of variation (CV)1.2325453
Kurtosis-0.14691886
Mean22.121915
Median Absolute Deviation (MAD)9.34
Skewness1.1279071
Sum194429.51
Variance743.44901
MonotonicityNot monotonic
2024-06-11T20:46:10.926523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2158
 
24.6%
0.14 19
 
0.2%
0.7 15
 
0.2%
10 15
 
0.2%
0.15 15
 
0.2%
0.05 14
 
0.2%
0.2 14
 
0.2%
5.1 13
 
0.1%
0.04 12
 
0.1%
9.2 12
 
0.1%
Other values (3312) 6502
74.0%
ValueCountFrequency (%)
0 2158
24.6%
0.01 9
 
0.1%
0.02 5
 
0.1%
0.03 12
 
0.1%
0.04 12
 
0.1%
0.05 14
 
0.2%
0.06 9
 
0.1%
0.07 6
 
0.1%
0.08 5
 
0.1%
0.09 8
 
0.1%
ValueCountFrequency (%)
100 2
< 0.1%
99.98 1
< 0.1%
99.8 1
< 0.1%
99.77 1
< 0.1%
99.53 1
< 0.1%
99.1 1
< 0.1%
99 1
< 0.1%
98.94 1
< 0.1%
98.9 1
< 0.1%
98.6 1
< 0.1%
Distinct662
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size541.6 KiB
2024-06-11T20:46:11.159330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length20
Median length17
Mean length6.0830584
Min length1

Characters and Unicode

Total characters53464
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique228 ?
Unique (%)2.6%

Sample

1st rowCORNSTARCH
2nd rowNUTS
3rd rowEGGPLANT
4th rowTEFF
5th rowSHERBET
ValueCountFrequency (%)
beef 967
 
11.0%
cereals 354
 
4.0%
pork 336
 
3.8%
lamb 295
 
3.4%
beverages 282
 
3.2%
babyfood 243
 
2.8%
fish 238
 
2.7%
chicken 214
 
2.4%
soup 176
 
2.0%
campbell's 156
 
1.8%
Other values (652) 5528
62.9%
2024-06-11T20:46:11.520593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 7960
14.9%
A 5023
 
9.4%
S 4824
 
9.0%
R 3413
 
6.4%
O 3103
 
5.8%
B 2959
 
5.5%
C 2750
 
5.1%
L 2633
 
4.9%
I 2417
 
4.5%
N 2326
 
4.4%
Other values (20) 16056
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 7960
14.9%
A 5023
 
9.4%
S 4824
 
9.0%
R 3413
 
6.4%
O 3103
 
5.8%
B 2959
 
5.5%
C 2750
 
5.1%
L 2633
 
4.9%
I 2417
 
4.5%
N 2326
 
4.4%
Other values (20) 16056
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 7960
14.9%
A 5023
 
9.4%
S 4824
 
9.0%
R 3413
 
6.4%
O 3103
 
5.8%
B 2959
 
5.5%
C 2750
 
5.1%
L 2633
 
4.9%
I 2417
 
4.5%
N 2326
 
4.4%
Other values (20) 16056
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 7960
14.9%
A 5023
 
9.4%
S 4824
 
9.0%
R 3413
 
6.4%
O 3103
 
5.8%
B 2959
 
5.5%
C 2750
 
5.1%
L 2633
 
4.9%
I 2417
 
4.5%
N 2326
 
4.4%
Other values (20) 16056
30.0%

PC1
Real number (ℝ)

HIGH CORRELATION 

Distinct8659
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.9806914 × 10-17
Minimum-1.8049219
Maximum26.726427
Zeros0
Zeros (%)0.0%
Negative5752
Negative (%)65.4%
Memory size68.8 KiB
2024-06-11T20:46:11.639894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1.8049219
5-th percentile-1.6066339
Q1-1.0274046
median-0.33966089
Q30.25496409
95-th percentile3.4014121
Maximum26.726427
Range28.531349
Interquartile range (IQR)1.2823687

Descriptive statistics

Standard deviation1.8762491
Coefficient of variation (CV)-9.4726978 × 1016
Kurtosis32.003339
Mean-1.9806914 × 10-17
Median Absolute Deviation (MAD)0.64152381
Skewness4.3273325
Sum-1.6379259 × 10-13
Variance3.5203108
MonotonicityNot monotonic
2024-06-11T20:46:11.748435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.804921949 47
 
0.5%
-1.790095628 5
 
0.1%
-0.2886630851 4
 
< 0.1%
-0.1614142229 4
 
< 0.1%
-1.802273456 3
 
< 0.1%
-0.1066425081 3
 
< 0.1%
-0.0398578533 3
 
< 0.1%
-0.1492671237 3
 
< 0.1%
-1.301735258 3
 
< 0.1%
-0.03275490061 3
 
< 0.1%
Other values (8649) 8711
99.1%
ValueCountFrequency (%)
-1.804921949 47
0.5%
-1.804836508 2
 
< 0.1%
-1.804665627 2
 
< 0.1%
-1.803372395 1
 
< 0.1%
-1.802857475 1
 
< 0.1%
-1.802761033 1
 
< 0.1%
-1.802385221 2
 
< 0.1%
-1.802273456 3
 
< 0.1%
-1.802119228 1
 
< 0.1%
-1.80205968 1
 
< 0.1%
ValueCountFrequency (%)
26.72642683 1
< 0.1%
25.06742958 1
< 0.1%
22.81213685 1
< 0.1%
22.5486972 1
< 0.1%
21.72250295 1
< 0.1%
18.83363543 1
< 0.1%
18.75567324 1
< 0.1%
17.92970034 1
< 0.1%
17.55644807 1
< 0.1%
17.19660667 1
< 0.1%

PC2
Real number (ℝ)

HIGH CORRELATION 

Distinct8659
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4772182 × 10-17
Minimum-7.4436381
Maximum21.228624
Zeros0
Zeros (%)0.0%
Negative5904
Negative (%)67.2%
Memory size68.8 KiB
2024-06-11T20:46:11.855120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-7.4436381
5-th percentile-1.1016073
Q1-0.71514456
median-0.39947707
Q30.28872858
95-th percentile2.0549926
Maximum21.228624
Range28.672262
Interquartile range (IQR)1.0038731

Descriptive statistics

Standard deviation1.4678302
Coefficient of variation (CV)2.6798827 × 1016
Kurtosis21.482924
Mean5.4772182 × 10-17
Median Absolute Deviation (MAD)0.38746268
Skewness3.6388754
Sum4.0700776 × 10-13
Variance2.1545255
MonotonicityNot monotonic
2024-06-11T20:46:11.963649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.6376583161 47
 
0.5%
-0.6389480721 5
 
0.1%
-0.3902364418 4
 
< 0.1%
-0.6888310551 4
 
< 0.1%
-0.6319100357 3
 
< 0.1%
-0.176139585 3
 
< 0.1%
-0.1690470416 3
 
< 0.1%
-0.6187880973 3
 
< 0.1%
-0.336678715 3
 
< 0.1%
-0.5654424875 3
 
< 0.1%
Other values (8649) 8711
99.1%
ValueCountFrequency (%)
-7.443638128 1
< 0.1%
-7.051411867 1
< 0.1%
-6.472121079 1
< 0.1%
-5.790940639 1
< 0.1%
-5.706326979 1
< 0.1%
-5.350007461 1
< 0.1%
-5.064014799 1
< 0.1%
-5.022783656 1
< 0.1%
-4.675175338 1
< 0.1%
-4.65529269 1
< 0.1%
ValueCountFrequency (%)
21.22862396 1
< 0.1%
12.00654669 1
< 0.1%
11.97292514 1
< 0.1%
11.73367099 1
< 0.1%
11.62170584 1
< 0.1%
11.57164334 1
< 0.1%
11.52162568 1
< 0.1%
11.44044286 1
< 0.1%
11.21476532 1
< 0.1%
11.12733672 1
< 0.1%

PC3
Real number (ℝ)

HIGH CORRELATION 

Distinct8659
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2337819 × 10-18
Minimum-12.117817
Maximum20.441154
Zeros0
Zeros (%)0.0%
Negative5430
Negative (%)61.8%
Memory size68.8 KiB
2024-06-11T20:46:12.074178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-12.117817
5-th percentile-1.4779008
Q1-0.57676457
median-0.19567817
Q30.61896853
95-th percentile1.4827926
Maximum20.441154
Range32.558972
Interquartile range (IQR)1.1957331

Descriptive statistics

Standard deviation1.3845175
Coefficient of variation (CV)4.2814188 × 1017
Kurtosis44.306246
Mean3.2337819 × 10-18
Median Absolute Deviation (MAD)0.57248946
Skewness4.0224336
Sum1.9939606 × 10-13
Variance1.9168886
MonotonicityNot monotonic
2024-06-11T20:46:12.187709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1271608279 47
 
0.5%
-0.1234610174 5
 
0.1%
0.8876355582 4
 
< 0.1%
0.6294233588 4
 
< 0.1%
-0.09664367516 3
 
< 0.1%
1.076659166 3
 
< 0.1%
1.167365969 3
 
< 0.1%
0.6164140516 3
 
< 0.1%
-0.1631251655 3
 
< 0.1%
0.8727664692 3
 
< 0.1%
Other values (8649) 8711
99.1%
ValueCountFrequency (%)
-12.11781724 1
< 0.1%
-9.664410804 1
< 0.1%
-9.65110486 1
< 0.1%
-8.742827143 1
< 0.1%
-8.392102591 1
< 0.1%
-7.392753257 1
< 0.1%
-7.271978906 1
< 0.1%
-6.330581697 1
< 0.1%
-5.961664629 1
< 0.1%
-5.767620691 1
< 0.1%
ValueCountFrequency (%)
20.44115436 1
< 0.1%
17.95333391 1
< 0.1%
17.58532021 1
< 0.1%
17.20690361 1
< 0.1%
17.09137011 1
< 0.1%
16.44027128 1
< 0.1%
15.90926346 1
< 0.1%
15.56564926 1
< 0.1%
15.09974805 1
< 0.1%
14.49207496 1
< 0.1%

PC4
Real number (ℝ)

HIGH CORRELATION 

Distinct8660
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.616891 × 10-17
Minimum-29.776412
Maximum33.660092
Zeros0
Zeros (%)0.0%
Negative4658
Negative (%)53.0%
Memory size68.8 KiB
2024-06-11T20:46:12.296743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-29.776412
5-th percentile-1.1125412
Q1-0.48290491
median-0.067438866
Q30.35245439
95-th percentile1.4319032
Maximum33.660092
Range63.436504
Interquartile range (IQR)0.8353593

Descriptive statistics

Standard deviation1.2252703
Coefficient of variation (CV)7.5779399 × 1016
Kurtosis152.09256
Mean1.616891 × 10-17
Median Absolute Deviation (MAD)0.4180527
Skewness1.3453654
Sum1.0280665 × 10-13
Variance1.5012872
MonotonicityNot monotonic
2024-06-11T20:46:12.408272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.23915271 47
 
0.5%
0.2302695644 5
 
0.1%
-0.2849515955 4
 
< 0.1%
-0.383609886 4
 
< 0.1%
-0.5881194822 3
 
< 0.1%
-0.3895045366 3
 
< 0.1%
0.23041252 3
 
< 0.1%
-0.6042003176 3
 
< 0.1%
0.4295992784 3
 
< 0.1%
-0.5312954204 3
 
< 0.1%
Other values (8650) 8711
99.1%
ValueCountFrequency (%)
-29.77641186 1
< 0.1%
-22.54910239 1
< 0.1%
-22.10971434 1
< 0.1%
-12.39127447 1
< 0.1%
-9.525780207 1
< 0.1%
-7.317605016 1
< 0.1%
-6.21231403 1
< 0.1%
-6.044166601 1
< 0.1%
-5.779507381 1
< 0.1%
-5.627512203 1
< 0.1%
ValueCountFrequency (%)
33.66009182 1
< 0.1%
18.36441107 1
< 0.1%
14.300134 1
< 0.1%
14.00604072 1
< 0.1%
13.28247051 1
< 0.1%
12.21753023 1
< 0.1%
12.14960918 1
< 0.1%
11.59226065 1
< 0.1%
10.75197111 1
< 0.1%
10.20003586 1
< 0.1%

PC5
Real number (ℝ)

Distinct8659
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.7785801 × 10-17
Minimum-17.087263
Maximum35.872656
Zeros0
Zeros (%)0.0%
Negative4783
Negative (%)54.4%
Memory size68.8 KiB
2024-06-11T20:46:12.524804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-17.087263
5-th percentile-0.92780187
Q1-0.27958935
median-0.039494932
Q30.20157108
95-th percentile1.1389408
Maximum35.872656
Range52.959919
Interquartile range (IQR)0.48116043

Descriptive statistics

Standard deviation1.1147983
Coefficient of variation (CV)-6.2679116 × 1016
Kurtosis181.89149
Mean-1.7785801 × 10-17
Median Absolute Deviation (MAD)0.24056518
Skewness5.6584554
Sum-2.1660451 × 10-13
Variance1.2427752
MonotonicityNot monotonic
2024-06-11T20:46:12.635535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1091094341 47
 
0.5%
-0.1106532 5
 
0.1%
-0.2738000324 4
 
< 0.1%
-0.4767711737 4
 
< 0.1%
-0.09853065407 3
 
< 0.1%
-0.3294374382 3
 
< 0.1%
-0.3509358596 3
 
< 0.1%
-0.3753126461 3
 
< 0.1%
-0.0251962333 3
 
< 0.1%
-0.2803701406 3
 
< 0.1%
Other values (8649) 8711
99.1%
ValueCountFrequency (%)
-17.08726288 1
< 0.1%
-14.08008788 1
< 0.1%
-14.0240976 1
< 0.1%
-10.69571032 1
< 0.1%
-10.08228512 1
< 0.1%
-9.202840878 1
< 0.1%
-8.690905354 1
< 0.1%
-8.561290757 1
< 0.1%
-8.407802424 1
< 0.1%
-7.62839789 1
< 0.1%
ValueCountFrequency (%)
35.8726563 1
< 0.1%
22.84437856 1
< 0.1%
17.64778236 1
< 0.1%
13.58291148 1
< 0.1%
12.67971546 1
< 0.1%
11.88351708 1
< 0.1%
11.57251034 1
< 0.1%
11.54133781 1
< 0.1%
11.2244622 1
< 0.1%
9.269004051 1
< 0.1%

PC6
Real number (ℝ)

Distinct8660
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1318237 × 10-17
Minimum-13.628151
Maximum22.22405
Zeros0
Zeros (%)0.0%
Negative5713
Negative (%)65.0%
Memory size68.8 KiB
2024-06-11T20:46:12.743608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-13.628151
5-th percentile-1.1083044
Q1-0.35700711
median-0.15675857
Q30.18887705
95-th percentile1.7842255
Maximum22.22405
Range35.8522
Interquartile range (IQR)0.54588416

Descriptive statistics

Standard deviation1.085882
Coefficient of variation (CV)9.5940912 × 1016
Kurtosis64.164621
Mean1.1318237 × 10-17
Median Absolute Deviation (MAD)0.25582898
Skewness3.1443743
Sum1.3175572 × 10-13
Variance1.1791396
MonotonicityNot monotonic
2024-06-11T20:46:12.859129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.2253903209 47
 
0.5%
-0.2215638516 5
 
0.1%
-0.231224869 4
 
< 0.1%
-0.2168392292 4
 
< 0.1%
-0.2612963556 3
 
< 0.1%
-0.1625106906 3
 
< 0.1%
-0.2307846384 3
 
< 0.1%
-0.3166920673 3
 
< 0.1%
0.07599151338 3
 
< 0.1%
-0.2251809391 3
 
< 0.1%
Other values (8650) 8711
99.1%
ValueCountFrequency (%)
-13.62815059 1
< 0.1%
-12.83591577 1
< 0.1%
-10.10252413 1
< 0.1%
-8.259712656 1
< 0.1%
-8.057239073 1
< 0.1%
-8.011681355 1
< 0.1%
-7.970112791 1
< 0.1%
-7.424525281 1
< 0.1%
-5.969250662 1
< 0.1%
-5.936716565 1
< 0.1%
ValueCountFrequency (%)
22.22404971 1
< 0.1%
18.23465009 1
< 0.1%
17.32838028 1
< 0.1%
16.132596 1
< 0.1%
16.03598845 1
< 0.1%
12.13607182 1
< 0.1%
11.82672742 1
< 0.1%
10.95344457 1
< 0.1%
10.83552302 1
< 0.1%
10.75477994 1
< 0.1%

PC7
Real number (ℝ)

Distinct8661
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0720928 × 10-17
Minimum-18.512367
Maximum28.733436
Zeros0
Zeros (%)0.0%
Negative4239
Negative (%)48.2%
Memory size68.8 KiB
2024-06-11T20:46:12.973659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-18.512367
5-th percentile-0.70907282
Q1-0.19539044
median0.013621678
Q30.19993578
95-th percentile0.74551972
Maximum28.733436
Range47.245803
Interquartile range (IQR)0.39532622

Descriptive statistics

Standard deviation0.99983959
Coefficient of variation (CV)3.2545878 × 1016
Kurtosis318.56893
Mean3.0720928 × 10-17
Median Absolute Deviation (MAD)0.19730542
Skewness9.2148378
Sum1.7133517 × 10-13
Variance0.9996792
MonotonicityNot monotonic
2024-06-11T20:46:13.093729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.2175247815 47
 
0.5%
-0.2160325981 5
 
0.1%
0.05036656663 4
 
< 0.1%
0.2192178721 4
 
< 0.1%
0.09436204125 3
 
< 0.1%
0.07843992115 3
 
< 0.1%
0.1989642726 3
 
< 0.1%
0.2237093852 3
 
< 0.1%
-0.09933129745 3
 
< 0.1%
-0.2155560436 3
 
< 0.1%
Other values (8651) 8711
99.1%
ValueCountFrequency (%)
-18.51236671 1
< 0.1%
-15.61208461 1
< 0.1%
-13.34050419 1
< 0.1%
-10.59116404 1
< 0.1%
-9.940018257 1
< 0.1%
-8.23020289 1
< 0.1%
-7.949456157 1
< 0.1%
-7.536547771 1
< 0.1%
-7.230982149 1
< 0.1%
-7.070534009 1
< 0.1%
ValueCountFrequency (%)
28.73343586 1
< 0.1%
28.27459014 1
< 0.1%
28.24483963 1
< 0.1%
20.80721502 1
< 0.1%
20.09881127 1
< 0.1%
17.24839588 1
< 0.1%
16.45150062 1
< 0.1%
13.81764831 1
< 0.1%
11.72806062 1
< 0.1%
10.98672683 1
< 0.1%

PC8
Real number (ℝ)

Distinct8659
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.9104037 × 10-17
Minimum-22.227382
Maximum35.75002
Zeros0
Zeros (%)0.0%
Negative5009
Negative (%)57.0%
Memory size68.8 KiB
2024-06-11T20:46:13.204258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-22.227382
5-th percentile-0.73007085
Q1-0.20256329
median-0.044369914
Q30.17179535
95-th percentile1.203886
Maximum35.75002
Range57.977402
Interquartile range (IQR)0.37435864

Descriptive statistics

Standard deviation0.99327745
Coefficient of variation (CV)-3.4128511 × 1016
Kurtosis300.12679
Mean-2.9104037 × 10-17
Median Absolute Deviation (MAD)0.18228367
Skewness-1.3191249
Sum-1.5637491 × 10-13
Variance0.9866001
MonotonicityNot monotonic
2024-06-11T20:46:13.313787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1433113731 47
 
0.5%
-0.1426631025 5
 
0.1%
-0.1683185479 4
 
< 0.1%
-0.2894713576 4
 
< 0.1%
-0.1393740257 3
 
< 0.1%
-0.1630163157 3
 
< 0.1%
-0.08604935328 3
 
< 0.1%
-0.3311749053 3
 
< 0.1%
0.03581514952 3
 
< 0.1%
-0.2863787642 3
 
< 0.1%
Other values (8649) 8711
99.1%
ValueCountFrequency (%)
-22.22738161 1
< 0.1%
-18.69575651 1
< 0.1%
-18.13433219 1
< 0.1%
-17.90204111 1
< 0.1%
-15.7344325 1
< 0.1%
-15.64577035 1
< 0.1%
-14.3615805 1
< 0.1%
-13.5909672 1
< 0.1%
-13.20921995 1
< 0.1%
-13.16510588 1
< 0.1%
ValueCountFrequency (%)
35.75002017 1
< 0.1%
4.966827922 1
< 0.1%
4.425007453 1
< 0.1%
3.946477638 1
< 0.1%
3.928642745 1
< 0.1%
3.640615582 1
< 0.1%
3.616226926 1
< 0.1%
3.6019209 1
< 0.1%
3.509527695 1
< 0.1%
3.418414496 1
< 0.1%

PC9
Real number (ℝ)

Distinct8661
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2126682 × 10-17
Minimum-15.156597
Maximum39.447735
Zeros0
Zeros (%)0.0%
Negative3566
Negative (%)40.6%
Memory size68.8 KiB
2024-06-11T20:46:13.421317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-15.156597
5-th percentile-1.2340732
Q1-0.21239027
median0.0885316
Q30.30888281
95-th percentile0.81540346
Maximum39.447735
Range54.604332
Interquartile range (IQR)0.52127308

Descriptive statistics

Standard deviation0.96798653
Coefficient of variation (CV)7.9822866 × 1016
Kurtosis343.54526
Mean1.2126682 × 10-17
Median Absolute Deviation (MAD)0.25061991
Skewness6.1031444
Sum1.0878798 × 10-13
Variance0.93699793
MonotonicityNot monotonic
2024-06-11T20:46:13.530229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1934643499 47
 
0.5%
0.192524924 5
 
0.1%
0.1823042979 4
 
< 0.1%
0.3356234779 4
 
< 0.1%
0.05991617132 3
 
< 0.1%
0.143978277 3
 
< 0.1%
0.3583809283 3
 
< 0.1%
0.3523488394 3
 
< 0.1%
0.1359057034 3
 
< 0.1%
0.17607937 3
 
< 0.1%
Other values (8651) 8711
99.1%
ValueCountFrequency (%)
-15.15659659 1
< 0.1%
-13.26491585 1
< 0.1%
-12.07458539 1
< 0.1%
-11.53291518 1
< 0.1%
-10.32620304 1
< 0.1%
-8.997292539 1
< 0.1%
-8.014640431 1
< 0.1%
-6.791536639 1
< 0.1%
-6.735951454 1
< 0.1%
-6.70270521 1
< 0.1%
ValueCountFrequency (%)
39.44773541 1
< 0.1%
11.76977701 1
< 0.1%
10.26638974 1
< 0.1%
8.389214758 1
< 0.1%
7.659964944 1
< 0.1%
7.381567821 1
< 0.1%
7.15437434 1
< 0.1%
7.003700488 1
< 0.1%
6.90845248 1
< 0.1%
5.304725216 1
< 0.1%

PC10
Real number (ℝ)

HIGH CORRELATION 

Distinct8660
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4675639 × 10-17
Minimum-13.37502
Maximum32.829228
Zeros0
Zeros (%)0.0%
Negative3317
Negative (%)37.7%
Memory size68.8 KiB
2024-06-11T20:46:13.639043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-13.37502
5-th percentile-1.1774069
Q1-0.20682254
median0.10559994
Q30.31970231
95-th percentile0.67929569
Maximum32.829228
Range46.204248
Interquartile range (IQR)0.52652485

Descriptive statistics

Standard deviation0.93136122
Coefficient of variation (CV)1.4400495 × 1016
Kurtosis209.83344
Mean6.4675639 × 10-17
Median Absolute Deviation (MAD)0.24826171
Skewness3.3675877
Sum5.0182081 × 10-13
Variance0.86743372
MonotonicityNot monotonic
2024-06-11T20:46:13.750840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2835187431 47
 
0.5%
0.2866191795 5
 
0.1%
0.4347979421 4
 
< 0.1%
0.3060021141 4
 
< 0.1%
0.3618802339 3
 
< 0.1%
0.4141010348 3
 
< 0.1%
0.2791835272 3
 
< 0.1%
0.3793667609 3
 
< 0.1%
0.3859965564 3
 
< 0.1%
0.322996252 3
 
< 0.1%
Other values (8650) 8711
99.1%
ValueCountFrequency (%)
-13.37501995 1
< 0.1%
-12.7240985 1
< 0.1%
-12.03168586 1
< 0.1%
-11.33060402 1
< 0.1%
-9.955747028 1
< 0.1%
-9.210615349 1
< 0.1%
-8.859894014 1
< 0.1%
-8.511868033 1
< 0.1%
-8.498120813 1
< 0.1%
-8.476776435 1
< 0.1%
ValueCountFrequency (%)
32.82922767 1
< 0.1%
10.87988207 1
< 0.1%
9.376151646 1
< 0.1%
9.36064912 1
< 0.1%
8.707423136 1
< 0.1%
7.625101393 1
< 0.1%
7.511839538 1
< 0.1%
6.986354344 1
< 0.1%
6.859442292 1
< 0.1%
6.491160146 1
< 0.1%

Interactions

2024-06-11T20:46:07.730074image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:53.574324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:54.597772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:55.815896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:56.847987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:57.888899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:59.128559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:00.153238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:01.200871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:02.249318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:03.527156image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:04.618216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:05.654891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:06.689969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:07.797837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:53.651795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:54.670919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:55.893184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:56.920508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:57.960915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:59.200078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:00.225759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:01.273157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:02.318838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:03.601678image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:04.691991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:05.727412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:06.761985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:07.871856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:53.727317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:54.743441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:55.972281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:56.993032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:58.034435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:59.273160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:00.302283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:01.348680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:02.391899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:03.678697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:04.769065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:05.803376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:06.838508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:07.942377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:53.798264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:54.812960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:56.040481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:57.065050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:58.106060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:59.342683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:00.374300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:01.421286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:02.462916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:03.752950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:04.838589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:05.874393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:06.909291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:08.016899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:53.872281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:54.887481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:56.113164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:57.139570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:58.178603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:59.415203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:00.450821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:01.495954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:02.537081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:03.831474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:04.913106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:05.948040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:06.983450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:08.424010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:53.943801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:55.134611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:56.187783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:57.215093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:58.251621image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:59.487724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:00.525346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:01.572885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:02.609602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:03.907996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:04.986631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:06.021560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:07.056971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:08.498532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:54.016323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:55.207151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:56.259798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:57.288614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:58.323146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:59.557740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:00.599090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:01.646679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:02.680623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:03.985129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:05.060645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:06.096203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:07.131149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:08.577171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:54.091844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:55.284672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:56.336322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:57.366633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:58.399666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:59.634502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:00.675110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:01.724925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:03.023725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:04.065146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:05.138038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:06.172222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:07.208948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:08.650691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:54.163996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:55.361692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:56.410844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:57.443153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:58.475870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:59.710024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:00.753243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:01.802280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:03.098246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:04.148914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:05.213665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:06.249745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:07.284000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:08.719212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:54.234514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:55.432213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:56.483131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:57.514674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:58.762451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:59.779637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:00.824764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:01.876297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:03.166326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:04.229759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:05.284193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:06.320270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:07.356520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:08.796734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:54.311037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:55.512736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:56.560332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:57.594197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:58.840974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:59.858656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:00.906287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:01.954713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:03.243849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:04.311293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:05.363211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:06.399790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:07.437043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:08.868751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:54.383558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:55.587259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:56.632932image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:57.668214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:58.913495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:59.932179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:00.980808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:02.028446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:03.315557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:04.386902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:05.435733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:06.471992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:07.510564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:08.939270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:54.455730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:55.668299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:56.705447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:57.741735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:58.986021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:00.006700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:01.055827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:02.102610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:03.387619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:04.464093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:05.509254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:06.546092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:07.584719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:09.009791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:54.528251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:55.742820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:56.777970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:57.817260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:45:59.057038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:00.083223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:01.130348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:02.178798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:03.458635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:04.540246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:05.582272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:06.618448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T20:46:07.658448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-06-11T20:46:13.834353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
PC1PC10PC2PC3PC4PC5PC6PC7PC8PC9caloriescarbohydrate_gprotein_gtotal_fat_grams
PC11.0000.0820.1720.144-0.431-0.182-0.1330.1330.136-0.0190.4970.0050.6470.409
PC100.0821.0000.0510.608-0.260-0.183-0.0660.042-0.240-0.190-0.265-0.5770.322-0.031
PC20.1720.0511.0000.211-0.3010.0660.1540.214-0.001-0.2320.579-0.2280.3670.878
PC30.1440.6080.2111.000-0.441-0.228-0.0430.091-0.201-0.149-0.166-0.7000.5620.120
PC4-0.431-0.260-0.301-0.4411.0000.0110.206-0.1770.062-0.123-0.1710.338-0.705-0.302
PC5-0.182-0.1830.066-0.2280.0111.0000.005-0.084-0.2160.090-0.0890.103-0.2420.031
PC6-0.133-0.0660.154-0.0430.2060.0051.0000.0280.134-0.4000.2330.286-0.1250.152
PC70.1330.0420.2140.091-0.177-0.0840.0281.0000.376-0.2780.185-0.1530.2070.211
PC80.136-0.240-0.001-0.2010.062-0.2160.1340.3761.000-0.2990.3180.2490.0370.132
PC9-0.019-0.190-0.232-0.149-0.1230.090-0.400-0.278-0.2991.000-0.2030.127-0.078-0.288
calories0.497-0.2650.579-0.166-0.171-0.0890.2330.1850.318-0.2031.0000.3710.3320.770
carbohydrate_g0.005-0.577-0.228-0.7000.3380.1030.286-0.1530.2490.1270.3711.000-0.404-0.084
protein_g0.6470.3220.3670.562-0.705-0.242-0.1250.2070.037-0.0780.332-0.4041.0000.473
total_fat_grams0.409-0.0310.8780.120-0.3020.0310.1520.2110.132-0.2880.770-0.0840.4731.000

Missing values

2024-06-11T20:46:09.118811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-11T20:46:09.309876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

namecaloriestotal_fat_gramsprotein_gcarbohydrate_gCategoryPC1PC2PC3PC4PC5PC6PC7PC8PC9PC10
0Cornstarch3810.10.2691.27CORNSTARCH-1.647091-0.658178-0.1664450.193537-0.128099-0.270291-0.255181-0.0991480.2295620.232153
1Nuts, pecans69172.09.1713.86NUTS2.1730425.169866-1.656052-0.301768-0.249505-1.5106930.1685190.4196960.783797-0.825249
2Eggplant, raw250.20.985.88EGGPLANT-1.133732-0.771836-0.4979180.246772-0.107108-0.424269-0.0807990.0850830.258346-0.046096
3Teff, uncooked3672.413.3073.13TEFF2.540549-1.032605-1.140901-0.996816-0.396015-0.683040-0.0648820.6961090.466783-0.163404
4Sherbet, orange1442.01.1030.40SHERBET-1.264300-0.641861-0.6019680.467947-0.1667080.6647970.1445560.490527-0.311870-0.388651
5Cauliflower, raw250.31.924.97CAULIFLOWER-0.909354-0.878998-0.4530320.3767960.034177-0.5179760.397345-0.3441840.0787920.263340
6Taro leaves, raw420.74.986.70TARO0.438624-0.993956-0.8756791.1663321.129473-1.312854-0.175857-0.355672-0.3914780.278511
7Lamb, raw, ground28223.016.560.00LAMB-0.3530121.0765370.763439-0.414198-0.114262-0.2280490.1811680.190644-0.5427440.213763
8Cheese, camembert30024.019.800.46CHEESE0.3684811.0338930.377328-1.1860421.0963621.0973970.295294-0.130765-0.9205590.436617
9Vegetarian fillets29018.023.009.00VEGETARIAN2.0304020.571543-0.0980880.056102-0.547242-0.0438440.231746-0.8043220.800163-0.421265
namecaloriestotal_fat_gramsprotein_gcarbohydrate_gCategoryPC1PC2PC3PC4PC5PC6PC7PC8PC9PC10
8779Beef, braised, cooked, all grades, trimmed to 1/8" fat, separable lean and fat, flat half, brisket28918.028.820.0BEEF0.1257870.6353631.300860-0.543254-0.678108-0.1482580.0794040.080403-0.4795970.502884
8780Beef, raw, select, trimmed to 1/8" fat, separable lean only, lip-on, boneless, rib eye steak/roast1486.422.550.0BEEF0.054082-0.2773301.089234-0.428389-0.762341-0.2702160.056788-0.0775310.0013010.516443
8781Beef, raw, choice, trimmed to 1/8" fat, separable lean only, lip-on, boneless, rib eye steak/roast1618.321.620.0BEEF0.061101-0.1509351.100520-0.368745-0.802455-0.2184680.035385-0.092505-0.0388920.518625
8782Oil, uses similar to 95 degree hard butter, confection fat, palm kernel (hydrogenated), industrial884100.00.000.0OIL-1.3516916.361679-0.1721930.1669110.8250511.7388321.7896692.133935-5.5625480.110599
8783Beef, raw, all grades, trimmed to 0" fat, separable lean and fat, boneless, top round steak, round1243.323.490.0BEEF0.047367-0.5272440.923638-0.353735-0.617635-0.1664570.111874-0.2008220.0720510.486277
8784Beef, raw, all grades, trimmed to 0" fat, separable lean and fat, boneless, top round roast, round1253.523.450.0BEEF0.045922-0.5152090.922604-0.353900-0.615158-0.1655950.113287-0.1989050.0678680.485413
8785Lamb, cooked, separable lean only, composite of trimmed retail cuts, frozen, imported, New Zealand2068.929.590.0LAMB-0.246957-0.0481080.929810-0.418810-0.210290-0.076078-0.0243020.054925-0.3724820.210014
8786Lamb, raw, separable lean and fat, composite of trimmed retail cuts, frozen, imported, New Zealand27723.016.740.0LAMB-0.6618981.0122600.587320-0.2235710.021455-0.0108950.1879240.221963-0.7444810.122516
8787Beef, raw, all grades, trimmed to 0" fat, separable lean only, boneless, eye of round roast, round1213.023.370.0BEEF-0.004727-0.5480530.930509-0.364613-0.565028-0.1989390.156510-0.2198420.1141070.444767
8788Beef, raw, all grades, trimmed to 0" fat, separable lean only, boneless, eye of round steak, round1213.023.370.0BEEF-0.004648-0.5480840.930633-0.364364-0.564796-0.1989870.156488-0.2198200.1141570.444632